POS0397 SHORT-TERM (2 YEARS) FRACTURE RISK PREDICTION: A MACHINE LEARNING APPROACH
Authors: G. Adami, M. D’souza, S. Vijayakumar, E. Grisan, A. Fassio, O. Viapiana, D. Gatti, M. Rossini
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Osteoporotic fractures continue to be a major cause of global health concerns around the world. The most common risk scores (FRAX, DeFRA) are evaluating this risk within a 10-yearstime window, which might not be suitable for evaluating shorter term risk, or quickly progressing patients. <h3>Objectives</h3> The objective of the present analysis is to develop 2-year fracture risk scoring models using Machine Learning (ML) techniques and compare their performance with the DeFRA tool. <h3>Methods</h3> Data were obtained from web-based fracture risk assessment tool (DeFRA) used in Italy. This tool is a derived version of the FRAX and can be accessed through a website (https://defra-osteoporosi.it/). 33 clinical and densitometric variables were used to compute the DeFRA risk score, with variable degree of completeness for each patient. After eliminating the attributes with a large number of missing values, and eliminating the patient with large outlier values for any of the attributes, the dataset was reduced to 2516 patients (with follow-up visits) and 15 attributes (Age, Weight, Height, BMI, Smoking, Alcohol, Familial Fractures/Osteoporosis, Previous femur or vertebra fractures, Previous other fractures, Comorbidities, Prednisone mg equivalent per day, T-score femoral-neck, T-score spine, Serum CTX levels, Anti-osteoporotic therapy prescribed). For the development of the machine-learning prediction, the dataset has been randomly divided into a train-set and a test-set, and the train-set has been synthetically balanced using Synthetic Minority Oversampling Technique (SMOTE). A Logistic Regression (LR) model and a Random Forest (RF) model have been optimized on the train-set for predicting the patients who incurred either a hip fracture, or any major osteoporotic fracture, or both in the 2 years following the baseline visit. DeFRA risk score is used as a benchmark for the prediction of either hip fracture or of any major fracture within 2-years. <h3>Results</h3> The DeFRA score, when used to predict the patients incurring a hip fracture within 2 years, has an Area Under the Receiver-Operating Curve(AUROC) of 0.52, compared to 0.76 for Random Forest and 0.81 for Logistic Regression. When looking at other major osteoporotic fractures, the AUROC is 0.59 for DeFRA, 0.77 for LR and 0.80 for RF, and when looking at any fracture the values are 0.58, 0.78, 0.80 for DeFRA, LR and RF respectively. Performance metrics including accuracy, sensitivity, and specificity are summarized in Table 1. <h3>Conclusion</h3> Currently usedrisk scores for osteoporotic fractures are developed within a 10-years time window, which might be toolong to stratify the most at risk-patients or those that are showing a fast-progressing disease. This isreflected by the poor predictive performance of DeFRA when looking at a 2-years time window. Machine-learning (either logistic regression or random forest) could greatly improve the predictive performance ofa scoring system to identify the patients most likely to incur an osteoporotic fracture in the short term,thus requiring shorter follow-ups. <h3>REFERENCES:</h3> NIL. <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> Giovanni Adami Speakers bureau: Eli Lilly, Theramex, UCB, Amgen, Galapagos, Fresenius Kabi, Megan D'souza: None declared, Shreyas Vijayakumar: None declared, Enrico Grisan: None declared, Angelo Fassio: None declared, Ombretta Viapiana: None declared, Davide Gatti: None declared, Maurizio Rossini: None declared.